From 27b386cb7596542a3296c32e41f7a5168b4d53be Mon Sep 17 00:00:00 2001 From: steniu01 Date: Tue, 18 Jul 2017 17:37:43 +0100 Subject: COMPMID-355 Implement 3x3 CL direct convolution Change-Id: I1b44dc375045964e65557f0ead57a7c12d6bf097 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/81418 Tested-by: Kaizen Reviewed-by: Anthony Barbier --- .../CL/kernels/CLDirectConvolutionLayerKernel.cpp | 171 +++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp (limited to 'src/core/CL/kernels') diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp new file mode 100644 index 0000000000..7f9e9d20e1 --- /dev/null +++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp @@ -0,0 +1,171 @@ +/* + * Copyright (c) 2017 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/IAccessWindow.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Validate.h" +#include "support/ToolchainSupport.h" + +using namespace arm_compute; + +template +CLDirectConvolutionLayerKernel::CLDirectConvolutionLayerKernel() + : _input(nullptr), _biases(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_pad_x(0), _conv_pad_y(0), _conv_stride_x(0), _conv_stride_y(0) +{ +} + +template +BorderSize CLDirectConvolutionLayerKernel::border_size() const +{ + return _border_size; +} + +template +void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) +{ + static_assert(kernel_size == 3, "Currently only 3x3 direct convolution is supported!"); + + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); + ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); + ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1)); + ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); + ARM_COMPUTE_ERROR_ON_MSG((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!"); + + ARM_COMPUTE_ERROR_ON(kernel_size != weights->info()->dimension(0)); + + if(biases != nullptr) + { + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); + ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); + ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); + } + + _conv_stride_x = std::get<0>(conv_info.stride()); + _conv_stride_y = std::get<1>(conv_info.stride()); + _conv_pad_x = std::get<0>(conv_info.pad()); + _conv_pad_y = std::get<1>(conv_info.pad()); + + _input = input; + _weights = weights; + _output = output; + _biases = biases; + _border_size = BorderSize(_conv_pad_y, _conv_pad_x); + + std::stringstream kernel_name; + std::set options; + kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size; + + options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + + options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); + + if(_biases != nullptr) + { + options.emplace("-DHAS_BIAS"); + } + + _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name.str(), options)); + + unsigned int idx = (_biases == nullptr) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor()); + _kernel.setArg(idx++, _weights->info()->strides_in_bytes()[3]); // weights_stride_w + _kernel.setArg(idx++, _weights->info()->dimension(2)); // filter depth + + // Using this local workgroup size gives better performance over others that have been tried. + _lws_hint = cl::NDRange(4, 1, 8); + + // Configure kernel window + Window win = calculate_max_window(*output->info()); + + unsigned int num_elems_read_per_iteration = 16 * _conv_stride_x; + unsigned int num_elems_written_per_iteration = 8; + + // Calculate right and bottom border + const int input_width = input->info()->dimension(0); + const int input_height = input->info()->dimension(1); + const int upper_bound_w = ceil_to_multiple(((output->info()->dimension(0) - 1) * _conv_stride_x + kernel_size), num_elems_read_per_iteration) - _conv_pad_x - input_width; + const int upper_bound_h = ((output->info()->dimension(1) - 1) * _conv_stride_y - _conv_pad_y + kernel_size) - input_height; + const int padding_right = std::max(upper_bound_w, static_cast(kernel_size)); + const int padding_bottom = std::max(upper_bound_h, static_cast(kernel_size)); + + // Create window and update padding + win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration)); + AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom); + + AccessWindowStatic weights_access(weights->info(), 0, 0, kernel_size, kernel_size); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); + update_window_and_padding(win, input_access, weights_access, output_access); + + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); + + ICLKernel::configure(win); +} + +template +void CLDirectConvolutionLayerKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + // Get initial windows + Window slice = window.first_slice_window_3D(); + Window win_in = window; + + win_in.adjust(Window::DimX, -_conv_pad_x, true); + win_in.adjust(Window::DimY, -_conv_pad_y, true); + win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x); + win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y); + + Window slice_in = win_in.first_slice_window_3D(); + + unsigned int idx1 = 2 * num_arguments_per_3D_tensor(); + add_3D_tensor_argument(idx1, _weights, slice); + + if(_biases != nullptr) + { + Window slice_biases; + slice_biases.use_tensor_dimensions(_biases->info()); + add_1D_tensor_argument(idx1, _biases, slice_biases); + } + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice_in); + add_3D_tensor_argument(idx, _output, slice); + + enqueue(queue, *this, slice, _lws_hint); + } + while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in)); +} + +template class arm_compute::CLDirectConvolutionLayerKernel<3>; -- cgit v1.2.1